


import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_SCREAMING_SNAKE_CASE     :			Union[str, Any]        =							2



class        A     :







     '''simple docstring'''







     def __init__(				self    :							Union[str, Any]			,				*,  # begin keyword-only arguments
         _UpperCamelCase    :							Optional[int]="<s>"			,				_UpperCamelCase    :							str="<pad>"			,				_UpperCamelCase    :							Any="</s>"			,				_UpperCamelCase    :							List[str]="<unk>"			,				_UpperCamelCase    :							List[str]=None			,				):
      _lowercase					,	_lowercase					,	_lowercase					,	_lowercase:       Any						=				bos, unk, pad, eos
      _lowercase:       List[str]						=				[]
      _lowercase:       Union[str, Any]						=				[]
      _lowercase:       List[Any]						=				{}
      _lowercase:       Optional[int]						=				self.add_symbol(_UpperCamelCase)
      _lowercase:       str						=				self.add_symbol(_UpperCamelCase)
      _lowercase:       List[str]						=				self.add_symbol(_UpperCamelCase)
      _lowercase:       str						=				self.add_symbol(_UpperCamelCase)
      if extra_special_symbols:
       for s in extra_special_symbols:
        self.add_symbol(_UpperCamelCase)
      _lowercase:       List[Any]						=				len(self.symbols)

     def __eq__(				self    :							Any			,				_UpperCamelCase    :							List[str]):
      return self.indices == other.indices

     def __getitem__(				self    :							Optional[Any]			,				_UpperCamelCase    :							List[Any]):
      if idx < len(self.symbols):
       return self.symbols[idx]
      return self.unk_word

     def __len__(				self    :							Tuple):
      return len(self.symbols)

     def __contains__(				self    :							Any			,				_UpperCamelCase    :							str):
      return sym in self.indices

     @classmethod
     def 					UpperCAmelCase__       (				cls    :							List[Any]			,				_UpperCamelCase    :							Optional[Any]):
      _lowercase:       Dict						=				cls()
      d.add_from_file(_UpperCamelCase)
      return d

     def 					UpperCAmelCase__       (				self    :							Optional[int]			,				_UpperCamelCase    :							List[str]			,				_UpperCamelCase    :							Any=1			,				_UpperCamelCase    :							Optional[Any]=False):
      if word in self.indices and not overwrite:
       _lowercase:       List[str]						=				self.indices[word]
       _lowercase:       str						=				self.count[idx] + n
       return idx
      else:
       _lowercase:       List[str]						=				len(self.symbols)
       _lowercase:       List[str]						=				idx
       self.symbols.append(_UpperCamelCase)
       self.count.append(_UpperCamelCase)
       return idx

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							List[Any]):
      return 0






     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							List[str]):
      if isinstance(_UpperCamelCase			,				_UpperCamelCase):
       try:
        with open(_UpperCamelCase			,				"r"			,				encoding="utf-8") as fd:
         self.add_from_file(_UpperCamelCase)
       except FileNotFoundError as fnfe:
        raise fnfe
       except UnicodeError:
        raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(_UpperCamelCase))
       return

      _lowercase:       Optional[int]						=				f.readlines()
      _lowercase:       int						=				self._load_meta(_UpperCamelCase)

      for line in lines[indices_start_line:]:
       try:
        _lowercase					,	_lowercase:       List[str]						=				line.rstrip().rsplit(" "			,				1)
        if field == "#fairseq:overwrite":
         _lowercase:       Optional[Any]						=				True
         _lowercase					,	_lowercase:       int						=				line.rsplit(" "			,				1)
        else:
         _lowercase:       Optional[Any]						=				False
        _lowercase:       Optional[Any]						=				int(_UpperCamelCase)
        _lowercase:       Dict						=				line
        if word in self and not overwrite:
         raise RuntimeError(
             "Duplicate word found when loading Dictionary: '{}'. "
             "Duplicate words can overwrite earlier ones by adding the "
             "#fairseq:overwrite flag at the end of the corresponding row "
             "in the dictionary file. If using the Camembert model, please "
             "download an updated copy of the model file.".format(_UpperCamelCase))
        self.add_symbol(_UpperCamelCase			,				n=_UpperCamelCase			,				overwrite=_UpperCamelCase)
       except ValueError:
        raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'")






def 							__lowerCAmelCase      (			__magic_name__      ):
 # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
 # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
 _lowercase:       List[str]						=				dict((re.sub(R"@@$" ,   "" ,   __magic_name__      ), v) if k.endswith("@@"      ) else (re.sub(R"$" ,   "</w>" ,   __magic_name__      ), v) for k, v in d.items()      )
 _lowercase:       Optional[int]						=				"<s> <pad> </s> <unk>".split()
 # restore the special tokens
 for k in keep_keys:
  del da[f"{k}</w>"]
  _lowercase:       List[str]						=				d[k]  # restore
 return da


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):
 # prep
 if not os.path.exists(__magic_name__      ):
  raise ValueError(f"path {biogpt_checkpoint_path} does not exist!"      )
 os.makedirs(__magic_name__ ,   exist_ok=__magic_name__      )
 print(f"Writing results to {pytorch_dump_folder_path}"      )

 # handle various types of models

 _lowercase:       int						=				os.path.join(__magic_name__ ,   "checkpoint.pt"      )
 if not os.path.isfile(__magic_name__      ):
  raise ValueError(f"path to the file {checkpoint_file} does not exist!"      )
 _lowercase:       Dict						=				torch.load(__magic_name__ ,   map_location="cpu"      )

 _lowercase:       Optional[int]						=				chkpt["cfg"]["model"]

 # dicts
 _lowercase:       int						=				os.path.join(__magic_name__ ,   "dict.txt"      )
 if not os.path.isfile(__magic_name__      ):
  raise ValueError(f"path to the file {dict_file} does not exist!"      )
 _lowercase:       Union[str, Any]						=				Dictionary.load(__magic_name__      )
 _lowercase:       Tuple						=				rewrite_dict_keys(src_dict.indices      )
 _lowercase:       Tuple						=				len(__magic_name__      )
 _lowercase:       List[Any]						=				os.path.join(__magic_name__ ,   VOCAB_FILES_NAMES["vocab_file"]      )
 print(f"Generating {src_vocab_file} of {src_vocab_size} records"      )
 with open(__magic_name__ ,   "w" ,   encoding="utf-8"      ) as f:
  f.write(json.dumps(__magic_name__ ,   ensure_ascii=__magic_name__ ,   indent=__magic_name__      )      )

 # merges_file (bpecodes)
 _lowercase:       Tuple						=				os.path.join(__magic_name__ ,   "bpecodes"      )
 if not os.path.isfile(__magic_name__      ):
  raise ValueError(f"path to the file {bpecodes_file} does not exist!"      )

 _lowercase:       Dict						=				os.path.join(__magic_name__ ,   VOCAB_FILES_NAMES["merges_file"]      )
 shutil.copyfile(__magic_name__ ,   __magic_name__      )

 # model config
 _lowercase:       Optional[Any]						=				os.path.join(__magic_name__ ,   "config.json"      )

 _lowercase:       Any						=				{
     "activation_dropout": args["activation_dropout"],
     "architectures": ["BioGptForCausalLM"],
     "attention_probs_dropout_prob": args["attention_dropout"],
     "bos_token_id": 0,
     "eos_token_id": 2,
     "hidden_act": args["activation_fn"],
     "hidden_dropout_prob": args["dropout"],
     "hidden_size": args["decoder_embed_dim"],
     "initializer_range": 0.02,
     "intermediate_size": args["decoder_ffn_embed_dim"],
     "layer_norm_eps": 1e-12,
     "layerdrop": args["decoder_layerdrop"],
     "max_position_embeddings": args["max_target_positions"],
     "model_type": "biogpt",
     "num_attention_heads": args["decoder_attention_heads"],
     "num_hidden_layers": args["decoder_layers"],
     "pad_token_id": 1,
     "scale_embedding": not args["no_scale_embedding"],
     "tie_word_embeddings": args["share_decoder_input_output_embed"],
     "vocab_size": src_vocab_size,
 }

 # good hparam defaults to start with

 print(f"Generating {biogpt_model_config_file}"      )
 with open(__magic_name__ ,   "w" ,   encoding="utf-8"      ) as f:
  f.write(json.dumps(__magic_name__ ,   ensure_ascii=__magic_name__ ,   indent=__magic_name__      )      )

 # tokenizer config
 _lowercase:       Dict						=				os.path.join(__magic_name__ ,   __magic_name__      )

 _lowercase:       Optional[int]						=				{
     "bos_token": "<s>",
     "eos_token": "</s>",
     "model_max_length": 1_0_2_4,
     "pad_token": "<pad>",
     "special_tokens_map_file": None,
     "tokenizer_class": "BioGptTokenizer",
     "unk_token": "<unk>",
 }

 print(f"Generating {biogpt_tokenizer_config_file}"      )
 with open(__magic_name__ ,   "w" ,   encoding="utf-8"      ) as f:
  f.write(json.dumps(__magic_name__ ,   ensure_ascii=__magic_name__ ,   indent=__magic_name__      )      )

 # model
 _lowercase:       Optional[int]						=				chkpt["model"]

 # remove unneeded keys
 _lowercase:       int						=				[
     "decoder.version",
 ]
 for k in ignore_keys:
  model_state_dict.pop(__magic_name__ ,   __magic_name__      )

 _lowercase:       int						=				list(model_state_dict.keys()      )
 for layer_name in layer_names:
  if layer_name.endswith("output_projection.weight"      ):
   _lowercase:       Tuple						=				model_state_dict.pop(__magic_name__      )
  else:
   _lowercase:       Any						=				model_state_dict.pop(__magic_name__      )

 _lowercase:       List[str]						=				BioGptConfig.from_pretrained(__magic_name__      )
 _lowercase:       int						=				BioGptForCausalLM(__magic_name__      )

 # check that it loads ok
 model_new.load_state_dict(__magic_name__      )

 # save
 _lowercase:       Tuple						=				os.path.join(__magic_name__ ,   __magic_name__      )
 print(f"Generating {pytorch_weights_dump_path}"      )
 torch.save(__magic_name__ ,   __magic_name__      )

 print("Conversion is done!"      )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--biogpt_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help=(
               'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
               ' bpecodes, etc.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       _SCREAMING_SNAKE_CASE     :			Any        =							parser.parse_args()
       convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
